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The current research paradigm for Huntington's disease is based on participants with overt clinical phenotypes and does not address its pathophysiology nor the biomarker changes that can precede by decades the functional decline. We have generated a new research framework to standardise clinical research and enable interventional studies earlier in the disease course. The Huntington's Disease Integrated Staging System (HD-ISS) comprises a biological research definition and evidence-based staging centred on biological, clinical, and functional assessments. We used a formal consensus method that involved representatives from academia, industry, and non-profit organisations. The HD-ISS characterises individuals for research purposes from birth, starting at Stage 0 (ie, individuals with the Huntington's disease genetic mutation without any detectable pathological change) by using a genetic definition of Huntington's disease. Huntington's disease progression is then marked by measurable indicators of underlying pathophysiology (Stage 1), a detectable clinical phenotype (Stage 2), and then decline in function (Stage 3). Individuals can be precisely classified into stages based on thresholds of stage-specific landmark assessments. We also demonstrated the internal validity of this system. The adoption of the HD-ISS could facilitate the design of clinical trials targeting populations before clinical motor diagnosis and enable data standardisation across ongoing and future studies.

Rare or orphan diseases often are inherited and overwhelmingly affect children. Many of these diseases have no treatments, are incurable, and have a devastating impact on patients and their families. Regulatory standards for drug approval for rare diseases must ensure that patients receive safe and efficacious treatments. However, regulatory bodies have shown flexibility in applying these standards to drug development in rare diseases, given the unique challenges that hinder efficient and effective traditional clinical trials, including low patient numbers, limited understanding of disease pathology and progression, variability in disease presentation, and a lack of established endpoints. To take steps toward improving rare disease clinical development strategies under current global regulatory statutes, Amicus Therapeutics, Inc. and BioNJ convened a 1-day meeting that included representatives from the Food and Drug Administration (FDA), biopharmaceutical industry, and not-for-profit agencies. The meeting focused on orphan diseases in pediatric and adult patients and was intended to identify potential strategies to overcome regulatory hurdles through open collaboration. During this meeting, several strategies were identified to minimize the limitations associated with low patient numbers in rare diseases, including the use of natural history to generate historical control data in comparisons, simulations, and identifying inclusion/exclusion criteria and appropriate endpoints. Novel approaches to clinical trial design were discussed to minimize patient exposure to placebo and to reduce the numbers of patients and clinical trials needed for providing substantial evidence. Novel statistical analysis approaches were also discussed to address the inherent challenges of small patient numbers. Areas of urgent unmet need were identified, including the need to develop registries that protect patient identities, to establish close collaboration and communication between the sponsor and regulatory bodies to address methodological and statistical challenges, to collaborate in pre-competitive opportunities within multiple sponsors and in conjunction with academia and disease-specific patient advocacy groups for optimal data sharing, and to develop harmonized guidelines for data extrapolation from source to target pediatric populations. Ultimately, these innovations will help in solving many regulatory challenges in rare disease drug development and encourage the availability of new treatments for patients with rare diseases.

Optimized frameworks for efficient and scalable deployment of digital health technologies (DHT) are needed to address existing bottlenecks and unlock the opportunities for remote monitoring and operationalizing decentralized trials. DHTs offer immense potential opportunities for transformation in drug development by providing remote, high frequency, longitudinal insights into physiological processes, and how participants feel and function. Currently, DHT-based drug development tool–related efforts have yielded valuable insights into effective practices and areas that need improvement. However, the development of the required infrastructure is a resource-intensive task, and its efficiency can be greatly enhanced by systematically identifying the required components and aligning them in ways that will avoid trial-and-error approaches by various stakeholders. In this perspective paper, we aim to highlight these crucial aspects required for supporting the rapid and large-scale deployment of DHTs. We propose the development of various standardized consensus frameworks to clearly lay out processes for various stakeholders and facilitate the seamless integration of the next generation of health care–sensing technologies into drug development.

Background Despite the high global disease burden of tuberculosis (TB), the disease caused by Mycobacterium tuberculosis  ( Mtb ) infection, novel treatments remain an urgent medical need. Development efforts continue to be hampered by the reliance on culture-based methods, which often take weeks to obtain due to the slow growth rate of Mtb . The availability of a “real-time” measure of treatment efficacy could accelerate TB drug development. Sputum lipoarabinomannan (LAM; an Mtb cell wall glycolipid) has promise as a pharmacodynamic biomarker of mycobacterial sputum load. Methods The present analysis evaluates LAM as a surrogate for Mtb burden in the sputum samples from 4 cohorts of a total of 776 participants. These include those from 2 cohorts of 558 non-TB and TB participants prior to the initiation of treatment (558 sputum samples), 1 cohort of 178 TB patients under a 14-day bactericidal activity trial with various mono- or multi-TB drug therapies, and 1 cohort of 40 TB patients with data from the first 56-day treatment of a standard 4-drug regimen. Results Regression analysis demonstrated that LAM was a predictor of colony-forming unit (CFU)/mL values obtained from the 14-day treatment cohort, with well-estimated model parameters (relative standard error ≤ 22.2%). Moreover, no changes in the relationship between LAM and CFU/mL were observed across the different treatments, suggesting that sputum LAM can be used to reasonably estimate the CFU/mL in the presence of treatment. The integrated analysis showed that sputum LAM also appears to be as good a predictor of time to Mycobacteria Growth Incubator Tube (MGIT) positivity as CFU/mL. As a binary readout, sputum LAM positivity is a strong predictor of solid media or MGIT culture positivity with an area-under-the-curve value of 0.979 and 0.976, respectively, from receiver-operator curve analysis. Conclusions Our results indicate that sputum LAM performs as a pharmacodynamic biomarker for rapid measurement of Mtb burden in sputum, and thereby may enable more efficient early phase clinical trial designs (e.g., adaptive designs) to compare candidate anti-TB regimens and streamline dose selection for use in pivotal trials. Trial registration NexGen EBA study (NCT02371681)

Tuberculosis (TB) remains a global health problem and there is an ongoing effort to develop more effective therapies and new combination regimes that can reduce duration of treatment. The purpose of this study was to demonstrate utility of a physiologically‐based pharmacokinetic modeling approach to predict plasma and lung concentrations of 11 compounds used or under development as TB therapies (bedaquiline [and N‐desmethyl bedaquiline], clofazimine, cycloserine, ethambutol, ethionamide, isoniazid, kanamycin, linezolid, pyrazinamide, rifampicin, and rifapentine). Model accuracy was assessed by comparison of simulated plasma pharmacokinetic parameters with healthy volunteer data for compounds administered alone or in combination. Eighty‐four percent (area under the curve [AUC]) and 91% (maximum concentration [Cmax]) of simulated mean values were within 1.5‐fold of the observed data and the simulated drug‐drug interaction ratios were within 1.5‐fold (AUC) and twofold (Cmax) of the observed data for nine (AUC) and eight (Cmax) of the 10 cases. Following satisfactory recovery of plasma concentrations in healthy volunteers, model accuracy was assessed further (where patients’ with TB data were available) by comparing clinical data with simulated lung concentrations (9 compounds) and simulated lung: plasma concentration ratios (7 compounds). The 5th–95th percentiles for the simulated lung concentration data recovered between 13% (isoniazid and pyrazinamide) and 88% (pyrazinamide) of the observed data points (Am J Respir Crit Care Med, 198, 2018, 1208; Nat Med, 21, 2015, 1223; PLoS Med, 16, 2019, e1002773). The impact of uncertain model parameters, such as the fraction of drug unbound in lung tissue mass (fumass), is discussed. Additionally, the variability associated with the patient lung concentration data, which was sparse and included extensive within‐subject, interlaboratory, and experimental variability (as well interindividual variability) is reviewed. All presented models are transparently documented and are available as open‐source to aid further research.

Clinical trials seeking to delay or prevent the onset of type 1 diabetes (T1D) face a series of pragmatic challenges. Despite more than 100 years since the discovery of insulin, teplizumab remains the only FDA‐approved therapy to delay progression from Stage 2 to Stage 3 T1D. To increase the efficiency of clinical trials seeking this goal, our project sought to inform T1D clinical trial designs by developing a disease progression model‐based clinical trial simulation tool. Using individual‐level data collected from the TrialNet Pathway to Prevention and The Environmental Determinants of Diabetes in the Young natural history studies, we previously developed a quantitative joint model to predict the time to T1D onset. We then applied trial‐specific inclusion/exclusion criteria, sample sizes in treatment and placebo arms, trial duration, assessment interval, and dropout rate. We implemented a function for presumed drug effects. To increase the size of the population pool, we generated virtual populations using multivariate normal distribution and ctree machine learning algorithms. As an output, power was calculated, which summarizes the probability of success, showing a statistically significant difference in the time distribution until the T1D diagnosis between the two arms. Using this tool, power curves can also be generated through iterations. The web‐based tool is publicly available: https://app.cop.ufl.edu/t1d/. Herein, we briefly describe the tool and provide instructions for simulating a planned clinical trial with two case studies. This tool will allow for improved clinical trial designs and accelerate efforts seeking to prevent or delay the onset of T1D.

The 21 st Century Cures Act requires FDA to expand its use of real-world evidence (RWE) to support approval of previously approved drugs for new disease indications and post-marketing study requirements. To address this need in neonates, the FDA and the Critical Path Institute (C-Path) established the International Neonatal Consortium (INC) to advance regulatory science and expedite neonatal drug development. FDA recently provided funding for INC to generate RWE to support regulatory decision making in neonatal drug development. One study is focused on developing a validated definition of bronchopulmonary dysplasia (BPD) in neonates. BPD is difficult to diagnose with diverse disease trajectories and few viable treatment options. Despite intense research efforts, limited understanding of the underlying disease pathobiology and disease projection continues in the context of a computable phenotype. It will be important to determine if: 1) a large, multisource aggregation of real-world data (RWD) will allow identification of validated risk factors and surrogate endpoints for BPD, and 2) the inclusion of these simulations will identify risk factors and surrogate endpoints for studies to prevent or treat BPD and its related long-term complications. The overall goal is to develop qualified, fit-for-purpose disease progression models which facilitate credible trial simulations while quantitatively capturing mechanistic relationships relevant for disease progression and the development of future treatments. The extent to which neonatal RWD can inform these models is unknown and its appropriateness cannot be guaranteed. A component of this approach is the critical evaluation of the various RWD sources for context-of use (COU)-driven models. The present manuscript defines a landscape of the data including targeted literature searches and solicitation of neonatal RWD sources from international stakeholders; analysis plans to develop a family of models of BPD in neonates, leveraging previous clinical trial experience and real-world patient data is also described.

Duchenne muscular dystrophy (DMD), a rare pediatric disease, presents numerous challenges when designing clinical trials, mainly due to the scarcity of available trial participants and the heterogeneity of disease progression. A quantitative clinical trial simulator (CTS) has been developed based on previously published five disease progression models describing each of the longitudinal changes in the velocity at which individuals can complete specified timed functional tests, frequently used as clinical trial efficacy endpoints (supine‐stand, 4‐stair climb, and 10 m walk/run test or 30‐foot walk/run test), as well as each of the longitudinal changes in forced vital capacity and North Star Ambulatory Assessment total score. The model‐based CTS allows researchers to optimize the selection of numerous trial parameters for designing trials for the five functional measures commonly used as endpoints in DMD clinical trials. This case report serves as a demonstration of the tool's functionality while providing an easy‐to‐follow guide for users to reference when preparing simulations of their own design. Two case studies, using input selection based on previous DMD clinical trials, provide realistic examples of how the tool can help optimize clinical trial design without the risk of decreasing statistical significance. This optimization allows researchers to mitigate the risk of designing trials that may be longer, larger, or more inclusive/exclusive than necessary.

Kidney transplantation is the preferred treatment for individuals with end‐stage kidney disease. From a modeling perspective, our understanding of kidney function trajectories after transplantation remains limited. Current modeling of kidney function post‐transplantation is focused on linear slopes or percent decline and often excludes the highly variable early timepoints post‐transplantation, where kidney function recovers and then stabilizes. Using estimated glomerular filtration rate (eGFR), a well‐known biomarker of kidney function, from an aggregated dataset of 4904 kidney transplant patients including both observational studies and clinical trials, we developed a longitudinal model of kidney function trajectories from time of transplant to 6 years post‐transplant. Our model is a nonlinear, mixed‐effects model built in NONMEM that captured both the recovery phase after kidney transplantation, where the graft recovers function, and the long‐term phase of stabilization and slow decline. Model fit was assessed using diagnostic plots and individual fits. Model performance, assessed via visual predictive checks, suggests accurate model predictions of eGFR at the median and lower 95% quantiles of eGFR, ranges which are of critical clinical importance for assessing loss of kidney function. Various clinically relevant covariates were also explored and found to improve the model. For example, transplant recipients of deceased donors recover function more slowly after transplantation and calcineurin inhibitor use promotes faster long‐term decay. Our work provides a generalizable, nonlinear model of kidney allograft function that will be useful for estimating eGFR up to 6 years post‐transplant in various clinically relevant populations.